Activity clustering and recognition is an important research trend about smart home. Result clusters obtained from training process are applied in recognizing, controlling as well as forecasting activities inside a sensor smart home. Activities differ from each other at typical characteristics such as sensor sets triggered or temporal ones. In this work, we present a smart home infrastructure and propose an unsupervised approach of clustering in-home activities based on temporal characteristics. Experiment results show that the new approach creates more flexible and accurate clustering results when compared to the smart home system named AALO reported in the research work of Enamul Hoque et al. Besides, in this work it is proven that clustering results affects significantly to the outcome of next-activity forecasting process via experimental comparison results.
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Activity clustering and recognition is an important research trend about smart home. Result clusters obtained from training process are applied in recognizing, controlling as well as forecasting activities inside a sensor smart home. Activities differ from each other at typical characteristics such as sensor sets triggered or temporal ones. In this work, we present a smart home infrastructure and propose an unsupervised approach of clustering in-home activities based on temporal characteristics. Experiment results show that the new approach creates more flexible and accurate clustering results when compared to the smart home system named AALO reported in the research work of Enamul Hoque et al. Besides, in this work it is proven that clustering results affects significantly to the outcome of next-activity forecasting process via experimental comparison results.
Duy Nguyen was born and raised in Binh Duong Province, Vietnam. Having got Bachelor degree in IT at the University of Technical Education in 2006. Graduated as Master of Information Technology at UIT, the National University of Vietnam. He currently works as a lecturer at the Faculty of Information Technology, Binh Duong University, Vietnam.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Activity clustering and recognition is an important research trend about smart home. Result clusters obtained from training process are applied in recognizing, controlling as well as forecasting activities inside a sensor smart home. Activities differ from each other at typical characteristics such as sensor sets triggered or temporal ones. In this work, we present a smart home infrastructure and propose an unsupervised approach of clustering in-home activities based on temporal characteristics. Experiment results show that the new approach creates more flexible and accurate clustering results when compared to the smart home system named AALO reported in the research work of Enamul Hoque et al. Besides, in this work it is proven that clustering results affects significantly to the outcome of next-activity forecasting process via experimental comparison results. 52 pp. Englisch. N° de réf. du vendeur 9783330081451
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Nguyen DuyDuy Nguyen was born and raised in Binh Duong Province, Vietnam. Having got Bachelor degree in IT at the University of Technical Education in 2006. Graduated as Master of Information Technology at UIT, the National Universit. N° de réf. du vendeur 151236964
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Activity clustering and recognition is an important research trend about smart home. Result clusters obtained from training process are applied in recognizing, controlling as well as forecasting activities inside a sensor smart home. Activities differ from each other at typical characteristics such as sensor sets triggered or temporal ones. In this work, we present a smart home infrastructure and propose an unsupervised approach of clustering in-home activities based on temporal characteristics. Experiment results show that the new approach creates more flexible and accurate clustering results when compared to the smart home system named AALO reported in the research work of Enamul Hoque et al. Besides, in this work it is proven that clustering results affects significantly to the outcome of next-activity forecasting process via experimental comparison results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. N° de réf. du vendeur 9783330081451
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Activity clustering and recognition is an important research trend about smart home. Result clusters obtained from training process are applied in recognizing, controlling as well as forecasting activities inside a sensor smart home. Activities differ from each other at typical characteristics such as sensor sets triggered or temporal ones. In this work, we present a smart home infrastructure and propose an unsupervised approach of clustering in-home activities based on temporal characteristics. Experiment results show that the new approach creates more flexible and accurate clustering results when compared to the smart home system named AALO reported in the research work of Enamul Hoque et al. Besides, in this work it is proven that clustering results affects significantly to the outcome of next-activity forecasting process via experimental comparison results. N° de réf. du vendeur 9783330081451
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Taschenbuch. Etat : Neu. A Novel Approach to Clustering Activities within Sensor Smart Homes | An approach of in-home activity recognition | Duy Nguyen (u. a.) | Taschenbuch | 52 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330081451 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 109127388
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